Instructions to use hwkwon/S-SOLAR-10.7B-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use hwkwon/S-SOLAR-10.7B-v1.5 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="hwkwon/S-SOLAR-10.7B-v1.5")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("hwkwon/S-SOLAR-10.7B-v1.5")
model = AutoModelForCausalLM.from_pretrained("hwkwon/S-SOLAR-10.7B-v1.5")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use hwkwon/S-SOLAR-10.7B-v1.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "hwkwon/S-SOLAR-10.7B-v1.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwkwon/S-SOLAR-10.7B-v1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/hwkwon/S-SOLAR-10.7B-v1.5

SGLang

How to use hwkwon/S-SOLAR-10.7B-v1.5 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "hwkwon/S-SOLAR-10.7B-v1.5" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwkwon/S-SOLAR-10.7B-v1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "hwkwon/S-SOLAR-10.7B-v1.5" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "hwkwon/S-SOLAR-10.7B-v1.5",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use hwkwon/S-SOLAR-10.7B-v1.5 with Docker Model Runner:
```
docker model run hf.co/hwkwon/S-SOLAR-10.7B-v1.5
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

S-SOLAR-10.7B

Model Description

This model is a fine-tuned version of chihoonlee10/T3Q-ko-solar-dpo-v5.0 with DeepSpeed.

Trained Data

Translated public data on HT and Generated data (about 110K)
Details are TBA

Prompt Template

### User: User query input

### Assistant:

License

This model is licensed under the cc-by-nc-4.0. which allows others to share and adapt the model for non-commercial purposes.

Downloads last month: 20

Safetensors

Model size

11B params

Tensor type

BF16

Model tree for hwkwon/S-SOLAR-10.7B-v1.5

Merges

2 models

Quantizations

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